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The vision of personalized medicine is to identify interventions that maintain or restore a person's health based on their individual biology. Medical digital twins, computational models that integrate a wide range of health-related data about a person and can be dynamically updated, are a key technology that can help guide medical decisions. Such medical digital twin models can be high-dimensional, multi-scale, and stochastic. To be practical for healthcare applications, they need to be simplified into low-dimensional metamodels that can be used for forecasting and optimal design of interventions. This paper introduces metamodeling algorithms for the purpose of optimal control applications. It uses agent-based models as a use case, a common model type in biomedicine for which there are no readily available optimal control algorithms. With systems of ordinary differential equations as metamodels, optimal control methods can be applied to the metamodels, and results can be lifted to the agent-based model.
Fonseca et al. (Thu,) studied this question.